Learning to Surf: Multiagent Systems for Adaptive Web Page Recommendation

Imagine a newspaper personalized for your tastes. Instead of a selection of articles chosen for a general audience by a human editor, a software agent picks items just for you, covering your particular topics of interest. Since there are no journalists at its disposal, the agent searches the Web for appropriate articles. Over time, it uses your feedback on recommended articles to build a model of your interests. This thesis investigates the design of "recommender systems" which create such personalized newspapers. Two research issues motivate this work and distinguish it from approaches usually taken by information retrieval or machine learning researchers. First, a recommender system will have many users, with overlapping interests. How can this be exploited? Second, each edition of a personalized newspaper consists of a small set of articles. Techniques for deciding on the relevance of individual articles are well known, but how is the composition of the set determined? One of the primary contributions of this research is an implemented architecture linking populations of adaptive software agents. Common interests among its users are used both to increase efficiency and scalability, and to improve the quality of recommendations. A novel interface infers document preferences by monitoring user drag-and-drop actions, and affords control over the composition of sets of recommendations. Results are presented from a variety of experiments: user tests measuring learning performance, simulation studies isolating particular tradeoffs, and usability tests investigating interaction designs.